An increasing number of studies are highlighting the importance of spatial dendritic branching in pyramidal neurons in the neocortex for supporting non-linear computation through localized synaptic integration. In particular, dendritic branches play a key role in temporal signal processing and feature detection. This is accomplished thanks to coincidence detection (CD) mechanisms enabled by the presence of synaptic delays that align temporally disparate inputs for effective integration. Computational studies on spiking neural networks further highlight the significance of delays for achieving spatio-temporal pattern recognition with pure feed-forward neural networks, without the need of resorting to recurrent architectures. In this work, we present “DenRAM”, the first realization of a feed-forward spiking neural network with dendritic compartments, implemented using analog electronic circuits integrated into a 130 nm technology node and coupled with Resistive Random Access Memory (RRAM) technology. DenRAM’s dendritic circuits use RRAM devices to implement both delays and synaptic weights in the network. By configuring the RRAM devices to reproduce bio-realistic timescales, and by exploiting their heterogeneity we experimentally demonstrate DenRAM’s ability to replicate synaptic delay profiles, and to efficiently implement CD for spatio-temporal pattern recognition. To validate the architecture, we conduct comprehensive system-level simulations on two representative temporal benchmarks, demonstrating DenRAM’s resilience to analog hardware noise, and its superior accuracy compared to recurrent architectures with an equivalent number of parameters. DenRAM not only brings rich temporal processing capabilities to neuromorphic architectures, but also reduces the memory footprint of edge devices, warrants high accuracy on temporal benchmarks, and represents a significant step-forward in low-power real-time signal processing technologies.

越来越多的研究强调新皮质锥体神经元空间树突分支对于通过局部突触整合支持非线性计算的重要性。特别是，树突分支在时间信号处理和特征检测中发挥着关键作用。这是通过突触延迟的存在实现的重合检测（CD）机制来实现的，该机制可以对齐时间上不同的输入以进行有效整合。对尖峰神经网络的计算研究进一步强调了延迟对于使用纯前馈神经网络实现时空模式识别的重要性，而无需诉诸循环架构。在这项工作中，我们提出了“DenRAM”，这是第一个实现具有树突室的前馈尖峰神经网络，使用集成到 130 nm 技术节点的模拟电子电路并结合电阻随机存取存储器 (RRAM) 技术来实现。 DenRAM 的树突电路使用 RRAM 器件来实现网络中的延迟和突触权重。通过配置 RRAM 设备来重现生物真实时间尺度，并利用其异质性，我们通过实验证明了 DenRAM 复制突触延迟曲线的能力，并有效实现 CD 进行时空模式识别。为了验证该架构，我们在两个代表性时间基准上进行了全面的系统级仿真，证明了 DenRAM 对模拟硬件噪声的恢复能力，以及与具有同等数量参数的循环架构相比的卓越准确性。 DenRAM 不仅为神经形态架构带来了丰富的时间处理能力，还减少了边缘设备的内存占用，保证了时间基准的高精度，代表了低功耗实时信号处理技术的重大进步。